Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model

Melissa De Iuliis, Omar Kammouh, Gian Paolo Cimellaro and Solomon Tesfamariam

Reliability Engineering and System Safety, 2021, vol. 208, issue C

Abstract: Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards can only be predicted to some extent, their impact can be managed by increasing the emergency response and reducing the vulnerability of infrastructure. In the context of risk management, the ability of infrastructure to withstand damage and re-establish their initial condition has recently gained prominence. Several resilience strategies have been investigated by numerous scholars to reduce disaster risk and evaluate the recovery time following disastrous events. A key parameter to quantify the seismic resilience of infrastructures is the Downtime (DT). Generally, DT assessment is challenging due to the parameters involved in the process. Such parameters are highly uncertain and therefore cannot be treated in a deterministic manner. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate the DT of selected infrastructure types following earthquakes. To demonstrate the applicability of the methodology, three scenarios are performed. Results show that the methodology is capable of providing good estimates of infrastructure DT despite the uncertainty of the parameters. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes in immediate post-event applications as well as to promptly recover damaged infrastructure.

Keywords: Downtime; Restoration; Lifelines; Infrastructure; Bayesian networks (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308139

DOI: 10.1016/j.ress.2020.107320

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